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    "# 深度学习简介\n",
    "\n",
    "你可能已经接触过编程，并开发过一两款程序。同时你可能读过关于深度学习或者机器学习的铺天盖地的报道，尽管很多时候它们被赋予了更广义的名字：人工智能。\n",
    "\n",
    "\n",
    "\n",
    "## 起源\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## 小结\n",
    "\n",
    "* consecutive\n",
    "\n",
    "\n",
    "\n",
    "## 练习\n",
    "\n",
    "* 你现在正在编写的代码有没有可以被“学习”的部分，也就是说，是否有可以被机器学习改进的部分？\n",
    "* 你在生活中有没有这样的场景：虽有许多展示如何解决问题的样例，但缺少自动解决问题的算法？它们也许是深度学习的最好猎物。\n",
    "* 如果把人工智能的发展看作是新一次工业革命，那么深度学习和数据的关系是否像是蒸汽机与煤炭的关系呢？为什么？\n",
    "* 端到端的训练方法还可以用在哪里？物理学，工程学还是经济学？\n",
    "* 为什么应该让深度网络模仿人脑结构？为什么不该让深度网络模仿人脑结构？\n",
    "\n",
    "\n",
    "\n",
    "## 参考文献\n",
    "\n",
    "[1] Machinery, C. (1950). Computing machinery and intelligence-AM Turing. Mind, 59(236), 433.\n",
    "\n",
    "[2] Hebb, D. O. (1949). The organization of behavior; a neuropsycholocigal theory. A Wiley Book in Clinical Psychology., 62-78.\n",
    "\n",
    "[3] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929-1958.\n",
    "\n"
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